The paper introduces a new framework for making machine learning explanations more understandable to humans.
problem Making machine learning explanations comprehensible and aligned with human preferences.
method Inspired by philosophy, cognitive science, and social sciences, the paper formalizes a framework using the concept of 'weight of evidence' from information theory.
result The framework produces intuitive and comprehensible explanations that align with human preferences.